The primary goal of this proposal is to identify and refine novel metabolic biomarkers that incrementally improve CVD risk prediction models and help predict the best obesity intervention to improve metabolic health, to begin to move towards a more personalized approach to obesity management for prevention of CVD. While obesity is a well-recognized risk factor for CVD-related morbidity and mortality, attributed to the increased prevalence of intermediate risk factors, there is heterogeneity in the prevalence of these risk factors in obese individuals.5 This disconnect leads to incomplete CVD risk prediction models and difficulty in identification of those obese individuals at greatest need for intensive therapeutic interventions for prevention of CVD events. Emerging molecular profiling technologies have begun to fill this gap. Building on a foundation of our previous work, we now propose here to use metabolomics profiling to develop biomarkers for identification of those obese individuals at greatest risk of CVD and, importantly, to build integrated clinico-metabolic models that would facilitate personalization of obesity interventions to prevent CVD. Our Specific Aims are (1) to test the hypothesis that previously identified metabolic signatures are associated with CVD-related clinical measures in obese individuals and with obesity interventions; (2) to use unbiased metabolomics approaches to identify novel biomarkers associated with these biomarkers; and (3) to develop an integrated clinico-molecular model incorporating clinical variables and metabolic biomarkers.